City branding and sustainable urban development
Bibliographic record
Abstract
City destination branding is "the steps city destination management organisations take to develop and communicate particular identities and personalities for city tourism that are different from those of all competitors” (Morrison and Maxim, 2022, p. 139). What is the relationship between city destination branding and sustainable urban development? In this chapter, the authors argue that it is when particular cities use sustainability or stewardship as part of their expressions of identity and personality. They want visitors and residents to associate them with sustainability. Several examples of countries that feature sustainability in destination branding include New Zealand, Costa Rica, Bhutan, Dominica, and Slovenia. However, more attention should be given to combining sustainability and branding at a city level, particularly in the context of smart cities and smart tourism destinations (Coca-Stefaniak, 2019; Huertas et al., 2021), although many urban areas are heavily engaged with sustainable development. Insch (2011) also noted limited research on green destination marketing and branding, and this chapter addresses these gaps in the literature. The main aim of this chapter was to explore the relationship between sustainable urban development and city destination branding. The specific objectives were to: 1. Compare place branding, city branding, and destination branding 2. Discuss green destination branding 3. Review the relationship between city destination branding and sustainable urban development 4. Analyse sustainable tourism city branding cases and derive indicators and actions for sustainable branding strategies 5. Describe a new paradigm for city destination branding
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".